Overview

Dataset statistics

Number of variables21
Number of observations41188
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory6.6 MiB
Average record size in memory168.0 B

Variable types

Categorical12
Numeric9

Warnings

Dataset has 12 (< 0.1%) duplicate rows Duplicates
"age has a high cardinality: 78 distinct values High cardinality
""emp.var.rate"" is highly correlated with ""euribor3m"" and 1 other fieldsHigh correlation
""euribor3m"" is highly correlated with ""emp.var.rate"" and 1 other fieldsHigh correlation
""nr.employed"" is highly correlated with ""emp.var.rate"" and 1 other fieldsHigh correlation
""previous"" has 35563 (86.3%) zeros Zeros

Reproduction

Analysis started2021-04-27 20:08:12.347717
Analysis finished2021-04-27 20:08:37.451937
Duration25.1 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

"age
Categorical

HIGH CARDINALITY

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
"31
 
1947
"32
 
1846
"33
 
1833
"36
 
1780
"35
 
1759
Other values (73)
32023 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123564
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row"56
2nd row"57
3rd row"37
4th row"40
5th row"56
ValueCountFrequency (%)
"311947
 
4.7%
"321846
 
4.5%
"331833
 
4.5%
"361780
 
4.3%
"351759
 
4.3%
"341745
 
4.2%
"301714
 
4.2%
"371475
 
3.6%
"291453
 
3.5%
"391432
 
3.5%
Other values (68)24204
58.8%
2021-04-27T15:08:37.804966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
311947
 
4.7%
321846
 
4.5%
331833
 
4.5%
361780
 
4.3%
351759
 
4.3%
341745
 
4.2%
301714
 
4.2%
371475
 
3.6%
291453
 
3.5%
391432
 
3.5%
Other values (68)24204
58.8%

Most occurring characters

ValueCountFrequency (%)
"41188
33.3%
320891
16.9%
414526
 
11.8%
511054
 
8.9%
29615
 
7.8%
65035
 
4.1%
14304
 
3.5%
94289
 
3.5%
74271
 
3.5%
84215
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number82376
66.7%
Other Punctuation41188
33.3%

Most frequent character per category

ValueCountFrequency (%)
320891
25.4%
414526
17.6%
511054
13.4%
29615
11.7%
65035
 
6.1%
14304
 
5.2%
94289
 
5.2%
74271
 
5.2%
84215
 
5.1%
04176
 
5.1%
ValueCountFrequency (%)
"41188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common123564
100.0%

Most frequent character per script

ValueCountFrequency (%)
"41188
33.3%
320891
16.9%
414526
 
11.8%
511054
 
8.9%
29615
 
7.8%
65035
 
4.1%
14304
 
3.5%
94289
 
3.5%
74271
 
3.5%
84215
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII123564
100.0%

Most frequent character per block

ValueCountFrequency (%)
"41188
33.3%
320891
16.9%
414526
 
11.8%
511054
 
8.9%
29615
 
7.8%
65035
 
4.1%
14304
 
3.5%
94289
 
3.5%
74271
 
3.5%
84215
 
3.4%

""job""
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""admin.""
10422 
""blue-collar""
9254 
""technician""
6743 
""services""
3969 
""management""
2924 
Other values (7)
7876 

Length

Max length17
Median length14
Mean length12.95522968
Min length10

Characters and Unicode

Total characters533600
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""housemaid""
2nd row""services""
3rd row""services""
4th row""admin.""
5th row""services""
ValueCountFrequency (%)
""admin.""10422
25.3%
""blue-collar""9254
22.5%
""technician""6743
16.4%
""services""3969
 
9.6%
""management""2924
 
7.1%
""retired""1720
 
4.2%
""entrepreneur""1456
 
3.5%
""self-employed""1421
 
3.5%
""housemaid""1060
 
2.6%
""unemployed""1014
 
2.5%
Other values (2)1205
 
2.9%
2021-04-27T15:08:38.154069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin10422
25.3%
blue-collar9254
22.5%
technician6743
16.4%
services3969
 
9.6%
management2924
 
7.1%
retired1720
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%
Other values (2)1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
"164752
30.9%
e47273
 
8.9%
n35547
 
6.7%
a33327
 
6.2%
l31618
 
5.9%
i30657
 
5.7%
c26709
 
5.0%
r21031
 
3.9%
m19765
 
3.7%
d16512
 
3.1%
Other values (15)106409
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter347751
65.2%
Other Punctuation175174
32.8%
Dash Punctuation10675
 
2.0%

Most frequent character per category

ValueCountFrequency (%)
e47273
13.6%
n35547
10.2%
a33327
9.6%
l31618
9.1%
i30657
8.8%
c26709
 
7.7%
r21031
 
6.0%
m19765
 
5.7%
d16512
 
4.7%
t14593
 
4.2%
Other values (12)70719
20.3%
ValueCountFrequency (%)
"164752
94.1%
.10422
 
5.9%
ValueCountFrequency (%)
-10675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin347751
65.2%
Common185849
34.8%

Most frequent character per script

ValueCountFrequency (%)
e47273
13.6%
n35547
10.2%
a33327
9.6%
l31618
9.1%
i30657
8.8%
c26709
 
7.7%
r21031
 
6.0%
m19765
 
5.7%
d16512
 
4.7%
t14593
 
4.2%
Other values (12)70719
20.3%
ValueCountFrequency (%)
"164752
88.6%
-10675
 
5.7%
.10422
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII533600
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
30.9%
e47273
 
8.9%
n35547
 
6.7%
a33327
 
6.2%
l31618
 
5.9%
i30657
 
5.7%
c26709
 
5.0%
r21031
 
3.9%
m19765
 
3.7%
d16512
 
3.1%
Other values (15)106409
19.9%

""marital""
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""married""
24928 
""single""
11568 
""divorced""
4612 
""unknown""
 
80

Length

Max length12
Median length11
Mean length10.83111586
Min length10

Characters and Unicode

Total characters446112
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""married""
2nd row""married""
3rd row""married""
4th row""married""
5th row""married""
ValueCountFrequency (%)
""married""24928
60.5%
""single""11568
28.1%
""divorced""4612
 
11.2%
""unknown""80
 
0.2%
2021-04-27T15:08:38.510070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:38.656716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married24928
60.5%
single11568
28.1%
divorced4612
 
11.2%
unknown80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
"164752
36.9%
r54468
 
12.2%
i41108
 
9.2%
e41108
 
9.2%
d34152
 
7.7%
m24928
 
5.6%
a24928
 
5.6%
n11808
 
2.6%
s11568
 
2.6%
g11568
 
2.6%
Other values (7)25724
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter281360
63.1%
Other Punctuation164752
36.9%

Most frequent character per category

ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.1%
m24928
8.9%
a24928
8.9%
n11808
 
4.2%
s11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (6)14156
 
5.0%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin281360
63.1%
Common164752
36.9%

Most frequent character per script

ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.1%
m24928
8.9%
a24928
8.9%
n11808
 
4.2%
s11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (6)14156
 
5.0%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII446112
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
36.9%
r54468
 
12.2%
i41108
 
9.2%
e41108
 
9.2%
d34152
 
7.7%
m24928
 
5.6%
a24928
 
5.6%
n11808
 
2.6%
s11568
 
2.6%
g11568
 
2.6%
Other values (7)25724
 
5.8%

""education""
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""university.degree""
12168 
""high.school""
9515 
""basic.9y""
6045 
""professional.course""
5243 
""basic.4y""
4176 
Other values (3)
4041 

Length

Max length23
Median length15
Mean length16.7109595
Min length11

Characters and Unicode

Total characters688291
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""basic.4y""
2nd row""high.school""
3rd row""high.school""
4th row""basic.6y""
5th row""high.school""
ValueCountFrequency (%)
""university.degree""12168
29.5%
""high.school""9515
23.1%
""basic.9y""6045
14.7%
""professional.course""5243
12.7%
""basic.4y""4176
 
10.1%
""basic.6y""2292
 
5.6%
""unknown""1731
 
4.2%
""illiterate""18
 
< 0.1%
2021-04-27T15:08:39.066584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:39.203219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
university.degree12168
29.5%
high.school9515
23.1%
basic.9y6045
14.7%
professional.course5243
12.7%
basic.4y4176
 
10.1%
basic.6y2292
 
5.6%
unknown1731
 
4.2%
illiterate18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
"164752
23.9%
e59194
 
8.6%
i51643
 
7.5%
s49925
 
7.3%
.39439
 
5.7%
o36490
 
5.3%
r34840
 
5.1%
h28545
 
4.1%
c27271
 
4.0%
y24681
 
3.6%
Other values (16)171511
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter471587
68.5%
Other Punctuation204191
29.7%
Decimal Number12513
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
e59194
12.6%
i51643
11.0%
s49925
10.6%
o36490
 
7.7%
r34840
 
7.4%
h28545
 
6.1%
c27271
 
5.8%
y24681
 
5.2%
n22604
 
4.8%
g21683
 
4.6%
Other values (11)114711
24.3%
ValueCountFrequency (%)
96045
48.3%
44176
33.4%
62292
 
18.3%
ValueCountFrequency (%)
"164752
80.7%
.39439
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
Latin471587
68.5%
Common216704
31.5%

Most frequent character per script

ValueCountFrequency (%)
e59194
12.6%
i51643
11.0%
s49925
10.6%
o36490
 
7.7%
r34840
 
7.4%
h28545
 
6.1%
c27271
 
5.8%
y24681
 
5.2%
n22604
 
4.8%
g21683
 
4.6%
Other values (11)114711
24.3%
ValueCountFrequency (%)
"164752
76.0%
.39439
 
18.2%
96045
 
2.8%
44176
 
1.9%
62292
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII688291
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
23.9%
e59194
 
8.6%
i51643
 
7.5%
s49925
 
7.3%
.39439
 
5.7%
o36490
 
5.3%
r34840
 
5.1%
h28545
 
4.1%
c27271
 
4.0%
y24681
 
3.6%
Other values (16)171511
24.9%

""default""
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""no""
32588 
""unknown""
8597 
""yes""
 
3

Length

Max length11
Median length6
Mean length7.043702049
Min length6

Characters and Unicode

Total characters290116
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""no""
2nd row""unknown""
3rd row""no""
4th row""no""
5th row""no""
ValueCountFrequency (%)
""no""32588
79.1%
""unknown""8597
 
20.9%
""yes""3
 
< 0.1%
2021-04-27T15:08:39.675953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:39.772694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
no32588
79.1%
unknown8597
 
20.9%
yes3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
"164752
56.8%
n58379
 
20.1%
o41185
 
14.2%
u8597
 
3.0%
k8597
 
3.0%
w8597
 
3.0%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation164752
56.8%
Lowercase Letter125364
43.2%

Most frequent character per category

ValueCountFrequency (%)
n58379
46.6%
o41185
32.9%
u8597
 
6.9%
k8597
 
6.9%
w8597
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common164752
56.8%
Latin125364
43.2%

Most frequent character per script

ValueCountFrequency (%)
n58379
46.6%
o41185
32.9%
u8597
 
6.9%
k8597
 
6.9%
w8597
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII290116
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
56.8%
n58379
 
20.1%
o41185
 
14.2%
u8597
 
3.0%
k8597
 
3.0%
w8597
 
3.0%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

""housing""
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""yes""
21576 
""no""
18622 
""unknown""
 
990

Length

Max length11
Median length7
Mean length6.644022531
Min length6

Characters and Unicode

Total characters273654
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""no""
2nd row""no""
3rd row""yes""
4th row""no""
5th row""no""
ValueCountFrequency (%)
""yes""21576
52.4%
""no""18622
45.2%
""unknown""990
 
2.4%
2021-04-27T15:08:40.076881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:40.181601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
yes21576
52.4%
no18622
45.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
"164752
60.2%
n21592
 
7.9%
y21576
 
7.9%
e21576
 
7.9%
s21576
 
7.9%
o19612
 
7.2%
u990
 
0.4%
k990
 
0.4%
w990
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation164752
60.2%
Lowercase Letter108902
39.8%

Most frequent character per category

ValueCountFrequency (%)
n21592
19.8%
y21576
19.8%
e21576
19.8%
s21576
19.8%
o19612
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common164752
60.2%
Latin108902
39.8%

Most frequent character per script

ValueCountFrequency (%)
n21592
19.8%
y21576
19.8%
e21576
19.8%
s21576
19.8%
o19612
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII273654
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
60.2%
n21592
 
7.9%
y21576
 
7.9%
e21576
 
7.9%
s21576
 
7.9%
o19612
 
7.2%
u990
 
0.4%
k990
 
0.4%
w990
 
0.4%

""loan""
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""no""
33950 
""yes""
6248 
""unknown""
 
990

Length

Max length11
Median length6
Mean length6.271875303
Min length6

Characters and Unicode

Total characters258326
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""no""
2nd row""no""
3rd row""no""
4th row""no""
5th row""yes""
ValueCountFrequency (%)
""no""33950
82.4%
""yes""6248
 
15.2%
""unknown""990
 
2.4%
2021-04-27T15:08:40.512716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:40.770028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
no33950
82.4%
yes6248
 
15.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
"164752
63.8%
n36920
 
14.3%
o34940
 
13.5%
y6248
 
2.4%
e6248
 
2.4%
s6248
 
2.4%
u990
 
0.4%
k990
 
0.4%
w990
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation164752
63.8%
Lowercase Letter93574
36.2%

Most frequent character per category

ValueCountFrequency (%)
n36920
39.5%
o34940
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common164752
63.8%
Latin93574
36.2%

Most frequent character per script

ValueCountFrequency (%)
n36920
39.5%
o34940
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII258326
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
63.8%
n36920
 
14.3%
o34940
 
13.5%
y6248
 
2.4%
e6248
 
2.4%
s6248
 
2.4%
u990
 
0.4%
k990
 
0.4%
w990
 
0.4%

""contact""
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""cellular""
26144 
""telephone""
15044 

Length

Max length13
Median length12
Mean length12.36525202
Min length12

Characters and Unicode

Total characters509300
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""telephone""
2nd row""telephone""
3rd row""telephone""
4th row""telephone""
5th row""telephone""
ValueCountFrequency (%)
""cellular""26144
63.5%
""telephone""15044
36.5%
2021-04-27T15:08:41.054268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:41.150012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cellular26144
63.5%
telephone15044
36.5%

Most occurring characters

ValueCountFrequency (%)
"164752
32.3%
l93476
18.4%
e71276
14.0%
c26144
 
5.1%
u26144
 
5.1%
a26144
 
5.1%
r26144
 
5.1%
t15044
 
3.0%
p15044
 
3.0%
h15044
 
3.0%
Other values (2)30088
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter344548
67.7%
Other Punctuation164752
32.3%

Most frequent character per category

ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin344548
67.7%
Common164752
32.3%

Most frequent character per script

ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII509300
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
32.3%
l93476
18.4%
e71276
14.0%
c26144
 
5.1%
u26144
 
5.1%
a26144
 
5.1%
r26144
 
5.1%
t15044
 
3.0%
p15044
 
3.0%
h15044
 
3.0%
Other values (2)30088
 
5.9%

""month""
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""may""
13769 
""jul""
7174 
""aug""
6178 
""jun""
5318 
""nov""
4101 
Other values (5)
4648 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters288316
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""may""
2nd row""may""
3rd row""may""
4th row""may""
5th row""may""
ValueCountFrequency (%)
""may""13769
33.4%
""jul""7174
17.4%
""aug""6178
15.0%
""jun""5318
 
12.9%
""nov""4101
 
10.0%
""apr""2632
 
6.4%
""oct""718
 
1.7%
""sep""570
 
1.4%
""mar""546
 
1.3%
""dec""182
 
0.4%
2021-04-27T15:08:41.456193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:41.564903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
may13769
33.4%
jul7174
17.4%
aug6178
15.0%
jun5318
 
12.9%
nov4101
 
10.0%
apr2632
 
6.4%
oct718
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
"164752
57.1%
a23125
 
8.0%
u18670
 
6.5%
m14315
 
5.0%
y13769
 
4.8%
j12492
 
4.3%
n9419
 
3.3%
l7174
 
2.5%
g6178
 
2.1%
o4819
 
1.7%
Other values (8)13603
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation164752
57.1%
Lowercase Letter123564
42.9%

Most frequent character per category

ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common164752
57.1%
Latin123564
42.9%

Most frequent character per script

ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII288316
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
57.1%
a23125
 
8.0%
u18670
 
6.5%
m14315
 
5.0%
y13769
 
4.8%
j12492
 
4.3%
n9419
 
3.3%
l7174
 
2.5%
g6178
 
2.1%
o4819
 
1.7%
Other values (8)13603
 
4.7%

""day_of_week""
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""thu""
8623 
""mon""
8514 
""wed""
8134 
""tue""
8090 
""fri""
7827 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters288316
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""mon""
2nd row""mon""
3rd row""mon""
4th row""mon""
5th row""mon""
ValueCountFrequency (%)
""thu""8623
20.9%
""mon""8514
20.7%
""wed""8134
19.7%
""tue""8090
19.6%
""fri""7827
19.0%
2021-04-27T15:08:42.030658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:42.144354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
thu8623
20.9%
mon8514
20.7%
wed8134
19.7%
tue8090
19.6%
fri7827
19.0%

Most occurring characters

ValueCountFrequency (%)
"164752
57.1%
t16713
 
5.8%
u16713
 
5.8%
e16224
 
5.6%
h8623
 
3.0%
m8514
 
3.0%
o8514
 
3.0%
n8514
 
3.0%
w8134
 
2.8%
d8134
 
2.8%
Other values (3)23481
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation164752
57.1%
Lowercase Letter123564
42.9%

Most frequent character per category

ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common164752
57.1%
Latin123564
42.9%

Most frequent character per script

ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII288316
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
57.1%
t16713
 
5.8%
u16713
 
5.8%
e16224
 
5.6%
h8623
 
3.0%
m8514
 
3.0%
o8514
 
3.0%
n8514
 
3.0%
w8134
 
2.8%
d8134
 
2.8%
Other values (3)23481
 
8.1%

""duration""
Real number (ℝ≥0)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.2850102
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Memory size321.9 KiB
2021-04-27T15:08:42.322877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile752.65
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.2792488
Coefficient of variation (CV)1.003849386
Kurtosis20.24793801
Mean258.2850102
Median Absolute Deviation (MAD)94
Skewness3.263141255
Sum10638243
Variance67225.72888
MonotocityNot monotonic
2021-04-27T15:08:42.503426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85170
 
0.4%
90170
 
0.4%
136168
 
0.4%
73167
 
0.4%
124164
 
0.4%
87162
 
0.4%
104161
 
0.4%
72161
 
0.4%
111160
 
0.4%
106159
 
0.4%
Other values (1534)39546
96.0%
ValueCountFrequency (%)
04
 
< 0.1%
13
 
< 0.1%
21
 
< 0.1%
33
 
< 0.1%
412
< 0.1%
ValueCountFrequency (%)
49181
< 0.1%
41991
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%

""campaign""
Real number (ℝ≥0)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.567592503
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Memory size321.9 KiB
2021-04-27T15:08:42.681949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.770013543
Coefficient of variation (CV)1.078836903
Kurtosis36.97979514
Mean2.567592503
Median Absolute Deviation (MAD)1
Skewness4.762506697
Sum105754
Variance7.672975028
MonotocityNot monotonic
2021-04-27T15:08:42.817587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
Other values (32)869
 
2.1%
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
ValueCountFrequency (%)
561
< 0.1%
432
< 0.1%
422
< 0.1%
411
< 0.1%
402
< 0.1%

""pdays""
Real number (ℝ≥0)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.475454
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Memory size321.9 KiB
2021-04-27T15:08:42.968187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.9109073
Coefficient of variation (CV)0.194198103
Kurtosis22.22946263
Mean962.475454
Median Absolute Deviation (MAD)0
Skewness-4.922189916
Sum39642439
Variance34935.68728
MonotocityNot monotonic
2021-04-27T15:08:43.119772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
99939673
96.3%
3439
 
1.1%
6412
 
1.0%
4118
 
0.3%
964
 
0.2%
261
 
0.1%
760
 
0.1%
1258
 
0.1%
1052
 
0.1%
546
 
0.1%
Other values (17)205
 
0.5%
ValueCountFrequency (%)
015
 
< 0.1%
126
 
0.1%
261
 
0.1%
3439
1.1%
4118
 
0.3%
ValueCountFrequency (%)
99939673
96.3%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%

""previous""
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1729629989
Minimum0
Maximum7
Zeros35563
Zeros (%)86.3%
Memory size321.9 KiB
2021-04-27T15:08:43.271367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4949010798
Coefficient of variation (CV)2.861311858
Kurtosis20.10881622
Mean0.1729629989
Median Absolute Deviation (MAD)0
Skewness3.832042243
Sum7124
Variance0.2449270788
MonotocityNot monotonic
2021-04-27T15:08:43.393050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
518
 
< 0.1%
470
 
0.2%
3216
0.5%

""poutcome""
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""nonexistent""
35563 
""failure""
4252 
""success""
 
1373

Length

Max length15
Median length15
Mean length14.45372439
Min length11

Characters and Unicode

Total characters595320
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""nonexistent""
2nd row""nonexistent""
3rd row""nonexistent""
4th row""nonexistent""
5th row""nonexistent""
ValueCountFrequency (%)
""nonexistent""35563
86.3%
""failure""4252
 
10.3%
""success""1373
 
3.3%
2021-04-27T15:08:43.756046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:43.871736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent35563
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
"164752
27.7%
n106689
17.9%
e76751
12.9%
t71126
11.9%
i39815
 
6.7%
s39682
 
6.7%
o35563
 
6.0%
x35563
 
6.0%
u5625
 
0.9%
f4252
 
0.7%
Other values (4)15502
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter430568
72.3%
Other Punctuation164752
 
27.7%

Most frequent character per category

ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%
ValueCountFrequency (%)
"164752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin430568
72.3%
Common164752
 
27.7%

Most frequent character per script

ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%
ValueCountFrequency (%)
"164752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII595320
100.0%

Most frequent character per block

ValueCountFrequency (%)
"164752
27.7%
n106689
17.9%
e76751
12.9%
t71126
11.9%
i39815
 
6.7%
s39682
 
6.7%
o35563
 
6.0%
x35563
 
6.0%
u5625
 
0.9%
f4252
 
0.7%
Other values (4)15502
 
2.6%

""emp.var.rate""
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08188550063
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Memory size321.9 KiB
2021-04-27T15:08:43.982441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.570959741
Coefficient of variation (CV)19.18483405
Kurtosis-1.062631525
Mean0.08188550063
Median Absolute Deviation (MAD)0.3
Skewness-0.7240955492
Sum3372.7
Variance2.467914506
MonotocityNot monotonic
2021-04-27T15:08:44.099128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.416234
39.4%
-1.89184
22.3%
1.17763
18.8%
-0.13683
 
8.9%
-2.91663
 
4.0%
-3.41071
 
2.6%
-1.7773
 
1.9%
-1.1635
 
1.5%
-3172
 
0.4%
-0.210
 
< 0.1%
ValueCountFrequency (%)
-3.41071
 
2.6%
-3172
 
0.4%
-2.91663
 
4.0%
-1.89184
22.3%
-1.7773
 
1.9%
ValueCountFrequency (%)
1.416234
39.4%
1.17763
18.8%
-0.13683
 
8.9%
-0.210
 
< 0.1%
-1.1635
 
1.5%

""cons.price.idx""
Real number (ℝ≥0)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57566437
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Memory size321.9 KiB
2021-04-27T15:08:44.217811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.578840049
Coefficient of variation (CV)0.00618579684
Kurtosis-0.8298085772
Mean93.57566437
Median Absolute Deviation (MAD)0.38
Skewness-0.2308876514
Sum3854194.464
Variance0.3350558023
MonotocityNot monotonic
2021-04-27T15:08:44.377387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9947763
18.8%
93.9186685
16.2%
92.8935794
14.1%
93.4445175
12.6%
94.4654374
10.6%
93.23616
8.8%
93.0752458
 
6.0%
92.201770
 
1.9%
92.963715
 
1.7%
92.431447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
92.201770
1.9%
92.379267
 
0.6%
92.431447
1.1%
92.469178
 
0.4%
92.649357
0.9%
ValueCountFrequency (%)
94.767128
 
0.3%
94.601204
 
0.5%
94.4654374
10.6%
94.215311
 
0.8%
94.199303
 
0.7%

""cons.conf.idx""
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.50260027
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Memory size321.9 KiB
2021-04-27T15:08:44.520003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.628197856
Coefficient of variation (CV)-0.1142691537
Kurtosis-0.3585583105
Mean-40.50260027
Median Absolute Deviation (MAD)4.4
Skewness0.3031798587
Sum-1668221.1
Variance21.4202154
MonotocityNot monotonic
2021-04-27T15:08:44.677615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.47763
18.8%
-42.76685
16.2%
-46.25794
14.1%
-36.15175
12.6%
-41.84374
10.6%
-423616
8.8%
-47.12458
 
6.0%
-31.4770
 
1.9%
-40.8715
 
1.7%
-26.9447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
-50.8128
 
0.3%
-50282
 
0.7%
-49.5204
 
0.5%
-47.12458
6.0%
-46.25794
14.1%
ValueCountFrequency (%)
-26.9447
1.1%
-29.8267
 
0.6%
-30.1357
0.9%
-31.4770
1.9%
-33172
 
0.4%

""euribor3m""
Real number (ℝ≥0)

HIGH CORRELATION

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621290813
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Memory size321.9 KiB
2021-04-27T15:08:44.836190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734447405
Coefficient of variation (CV)0.4789583313
Kurtosis-1.406802622
Mean3.621290813
Median Absolute Deviation (MAD)0.108
Skewness-0.7091879564
Sum149153.726
Variance3.0083078
MonotocityNot monotonic
2021-04-27T15:08:44.991769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572868
 
7.0%
4.9622613
 
6.3%
4.9632487
 
6.0%
4.9611902
 
4.6%
4.8561210
 
2.9%
4.9641175
 
2.9%
1.4051169
 
2.8%
4.9651071
 
2.6%
4.8641044
 
2.5%
4.961013
 
2.5%
Other values (306)24636
59.8%
ValueCountFrequency (%)
0.6348
 
< 0.1%
0.63543
0.1%
0.63614
 
< 0.1%
0.6376
 
< 0.1%
0.6387
 
< 0.1%
ValueCountFrequency (%)
5.0459
 
< 0.1%
57
 
< 0.1%
4.97172
 
0.4%
4.968992
2.4%
4.967643
1.6%

""nr.employed""
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.035911
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Memory size321.9 KiB
2021-04-27T15:08:45.133363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.25152767
Coefficient of variation (CV)0.01398316732
Kurtosis-0.003760375696
Mean5167.035911
Median Absolute Deviation (MAD)37.1
Skewness-1.044262407
Sum212819875.1
Variance5220.28325
MonotocityNot monotonic
2021-04-27T15:08:45.254041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.116234
39.4%
5099.18534
20.7%
51917763
18.8%
5195.83683
 
8.9%
5076.21663
 
4.0%
5017.51071
 
2.6%
4991.6773
 
1.9%
5008.7650
 
1.6%
4963.6635
 
1.5%
5023.5172
 
0.4%
ValueCountFrequency (%)
4963.6635
1.5%
4991.6773
1.9%
5008.7650
1.6%
5017.51071
2.6%
5023.5172
 
0.4%
ValueCountFrequency (%)
5228.116234
39.4%
5195.83683
 
8.9%
51917763
18.8%
5176.310
 
< 0.1%
5099.18534
20.7%

""y"""
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
""no"""
36548 
""yes"""
4640 

Length

Max length8
Median length7
Mean length7.112654171
Min length7

Characters and Unicode

Total characters292956
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row""no"""
2nd row""no"""
3rd row""no"""
4th row""no"""
5th row""no"""
ValueCountFrequency (%)
""no"""36548
88.7%
""yes"""4640
 
11.3%
2021-04-27T15:08:45.581168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T15:08:45.704837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
no36548
88.7%
yes4640
 
11.3%

Most occurring characters

ValueCountFrequency (%)
"205940
70.3%
n36548
 
12.5%
o36548
 
12.5%
y4640
 
1.6%
e4640
 
1.6%
s4640
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation205940
70.3%
Lowercase Letter87016
29.7%

Most frequent character per category

ValueCountFrequency (%)
n36548
42.0%
o36548
42.0%
y4640
 
5.3%
e4640
 
5.3%
s4640
 
5.3%
ValueCountFrequency (%)
"205940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common205940
70.3%
Latin87016
29.7%

Most frequent character per script

ValueCountFrequency (%)
n36548
42.0%
o36548
42.0%
y4640
 
5.3%
e4640
 
5.3%
s4640
 
5.3%
ValueCountFrequency (%)
"205940
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII292956
100.0%

Most frequent character per block

ValueCountFrequency (%)
"205940
70.3%
n36548
 
12.5%
o36548
 
12.5%
y4640
 
1.6%
e4640
 
1.6%
s4640
 
1.6%

Interactions

2021-04-27T15:08:23.897930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:24.080526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:24.251070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:24.425604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:24.588169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:24.760707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:24.922276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:25.088831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:25.276330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:25.464865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:25.644382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:25.822905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:25.984437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:26.153983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:26.313557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:26.476122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:26.653682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:26.822234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:26.982801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:27.168312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:27.426580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:27.583162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:27.743733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:27.895345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:28.056896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:28.237414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:28.408954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:28.612413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:28.796917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:28.967462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:29.140001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:29.315531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:29.489068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:29.654624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:29.810209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:29.960806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:30.125366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:30.276961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:30.439526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:30.588140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:30.763698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:30.925266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:31.087831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:31.247399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:31.425889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:31.577484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:31.744039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:31.891644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:32.049223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:32.203810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:32.362386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:32.536920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:32.718434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:32.878007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:33.037582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:33.183192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:33.444493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:33.611048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:33.765634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:33.929198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:34.093758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:34.239368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:34.391960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:34.537570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:34.713140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:34.875684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:35.055189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:35.216757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:35.396996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:35.555660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:35.732568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-27T15:08:35.892142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-27T15:08:45.806565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-27T15:08:46.088809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-27T15:08:46.355098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-27T15:08:46.855759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-27T15:08:47.266703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-27T15:08:36.265230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-27T15:08:37.058202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

"age""job""""marital""""education""""default""""housing""""loan""""contact""""month""""day_of_week""""duration""""campaign""""pdays""""previous""""poutcome""""emp.var.rate""""cons.price.idx""""cons.conf.idx""""euribor3m""""nr.employed""""y"""
0"56""housemaid""""married""""basic.4y""""no""""no""""no""""telephone""""may""""mon""26119990""nonexistent""1.193.994-36.44.8575191.0""no"""
1"57""services""""married""""high.school""""unknown""""no""""no""""telephone""""may""""mon""14919990""nonexistent""1.193.994-36.44.8575191.0""no"""
2"37""services""""married""""high.school""""no""""yes""""no""""telephone""""may""""mon""22619990""nonexistent""1.193.994-36.44.8575191.0""no"""
3"40""admin.""""married""""basic.6y""""no""""no""""no""""telephone""""may""""mon""15119990""nonexistent""1.193.994-36.44.8575191.0""no"""
4"56""services""""married""""high.school""""no""""no""""yes""""telephone""""may""""mon""30719990""nonexistent""1.193.994-36.44.8575191.0""no"""
5"45""services""""married""""basic.9y""""unknown""""no""""no""""telephone""""may""""mon""19819990""nonexistent""1.193.994-36.44.8575191.0""no"""
6"59""admin.""""married""""professional.course""""no""""no""""no""""telephone""""may""""mon""13919990""nonexistent""1.193.994-36.44.8575191.0""no"""
7"41""blue-collar""""married""""unknown""""unknown""""no""""no""""telephone""""may""""mon""21719990""nonexistent""1.193.994-36.44.8575191.0""no"""
8"24""technician""""single""""professional.course""""no""""yes""""no""""telephone""""may""""mon""38019990""nonexistent""1.193.994-36.44.8575191.0""no"""
9"25""services""""single""""high.school""""no""""yes""""no""""telephone""""may""""mon""5019990""nonexistent""1.193.994-36.44.8575191.0""no"""

Last rows

"age""job""""marital""""education""""default""""housing""""loan""""contact""""month""""day_of_week""""duration""""campaign""""pdays""""previous""""poutcome""""emp.var.rate""""cons.price.idx""""cons.conf.idx""""euribor3m""""nr.employed""""y"""
41178"62""retired""""married""""university.degree""""no""""no""""no""""cellular""""nov""""thu""483263""success""-1.194.767-50.81.0314963.6""yes"""
41179"64""retired""""divorced""""professional.course""""no""""yes""""no""""cellular""""nov""""fri""15139990""nonexistent""-1.194.767-50.81.0284963.6""no"""
41180"36""admin.""""married""""university.degree""""no""""no""""no""""cellular""""nov""""fri""25429990""nonexistent""-1.194.767-50.81.0284963.6""no"""
41181"37""admin.""""married""""university.degree""""no""""yes""""no""""cellular""""nov""""fri""28119990""nonexistent""-1.194.767-50.81.0284963.6""yes"""
41182"29""unemployed""""single""""basic.4y""""no""""yes""""no""""cellular""""nov""""fri""112191""success""-1.194.767-50.81.0284963.6""no"""
41183"73""retired""""married""""professional.course""""no""""yes""""no""""cellular""""nov""""fri""33419990""nonexistent""-1.194.767-50.81.0284963.6""yes"""
41184"46""blue-collar""""married""""professional.course""""no""""no""""no""""cellular""""nov""""fri""38319990""nonexistent""-1.194.767-50.81.0284963.6""no"""
41185"56""retired""""married""""university.degree""""no""""yes""""no""""cellular""""nov""""fri""18929990""nonexistent""-1.194.767-50.81.0284963.6""no"""
41186"44""technician""""married""""professional.course""""no""""no""""no""""cellular""""nov""""fri""44219990""nonexistent""-1.194.767-50.81.0284963.6""yes"""
41187"74""retired""""married""""professional.course""""no""""yes""""no""""cellular""""nov""""fri""23939991""failure""-1.194.767-50.81.0284963.6""no"""

Duplicate rows

Most frequent

"age""job""""marital""""education""""default""""housing""""loan""""contact""""month""""day_of_week""""duration""""campaign""""pdays""""previous""""poutcome""""emp.var.rate""""cons.price.idx""""cons.conf.idx""""euribor3m""""nr.employed""""y"""count
0"24""services""""single""""high.school""""no""""yes""""no""""cellular""""apr""""tue""11419990""nonexistent""-1.893.075-47.11.4235099.1""no"""2
1"27""technician""""single""""professional.course""""no""""no""""no""""cellular""""jul""""mon""33129990""nonexistent""1.493.918-42.74.9625228.1""no"""2
2"32""technician""""single""""professional.course""""no""""yes""""no""""cellular""""jul""""thu""12819990""nonexistent""1.493.918-42.74.9685228.1""no"""2
3"35""admin.""""married""""university.degree""""no""""yes""""no""""cellular""""may""""fri""34849990""nonexistent""-1.892.893-46.21.3135099.1""no"""2
4"36""retired""""married""""unknown""""no""""no""""no""""telephone""""jul""""thu""8819990""nonexistent""1.493.918-42.74.9665228.1""no"""2
5"39""admin.""""married""""university.degree""""no""""no""""no""""cellular""""nov""""tue""12329990""nonexistent""-0.193.200-42.04.1535195.8""no"""2
6"39""blue-collar""""married""""basic.6y""""no""""no""""no""""telephone""""may""""thu""12419990""nonexistent""1.193.994-36.44.8555191.0""no"""2
7"41""technician""""married""""professional.course""""no""""yes""""no""""cellular""""aug""""tue""12719990""nonexistent""1.493.444-36.14.9665228.1""no"""2
8"45""admin.""""married""""university.degree""""no""""no""""no""""cellular""""jul""""thu""25219990""nonexistent""-2.992.469-33.61.0725076.2""yes"""2
9"47""technician""""divorced""""high.school""""no""""yes""""no""""cellular""""jul""""thu""4339990""nonexistent""1.493.918-42.74.9625228.1""no"""2